{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "本代码适用于针对 WISE 2MASS vega星等数据计算流量（flux），并画出SED。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# WISE vega星等转流量\n",
    "\n",
    "def flux_wise_w1(mag_vega):\n",
    "    mag_ab = mag_vega + 2.699\n",
    "    F = 10**(-(mag_ab+48.60)/2.5) * 10**23\n",
    "    return F\n",
    "\n",
    "def flux_wise_w2(mag_vega):\n",
    "    mag_ab = mag_vega + 3.339\n",
    "    F = 10**(-(mag_ab+48.60)/2.5) * 10**23\n",
    "    return F\n",
    "\n",
    "def flux_wise_w3(mag_vega):\n",
    "    mag_ab = mag_vega + 5.174\n",
    "    F = 10**(-(mag_ab+48.60)/2.5) * 10**23\n",
    "    return F\n",
    "\n",
    "def flux_wise_w4(mag_vega):\n",
    "    mag_ab = mag_vega + 6.620\n",
    "    F = 10**(-(mag_ab+48.60)/2.5) * 10**23\n",
    "    return F"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2MASS vega星等转流量\n",
    "\n",
    "def flux_2mass_J(mag_vega):\n",
    "    mag_ab = mag_vega + 0.91\n",
    "    F = 10**(-(mag_ab+48.60)/2.5) * 10**23\n",
    "    return F\n",
    "\n",
    "def flux_2mass_H(mag_vega):\n",
    "    mag_ab = mag_vega + 1.39\n",
    "    F = 10**(-(mag_ab+48.60)/2.5) * 10**23\n",
    "    return F\n",
    "\n",
    "def flux_2mass_K(mag_vega):\n",
    "    mag_ab = mag_vega + 1.85\n",
    "    F = 10**(-(mag_ab+48.60)/2.5) * 10**23\n",
    "    return F"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 挑一个点多的画图\n",
    "# 导入数据：HERSCHEL flux 单位：mJy；WISE 2MASS vega星等\n",
    "data = pd.read_csv(\"flux_original.csv\")\n",
    "\n",
    "# 计算流量\n",
    "c = 3 * 10**14  # um/s\n",
    "wavelength_um = [70, 100, 160, 250, 350, 500, 3.4, 4.2, 12, 22, 1.23, 1.66, 2.15]\n",
    "nu = [c/i for i in wavelength_um]\n",
    "wavelength = [i*10000 for i in wavelength_um]\n",
    "flux = [data['herf_70'][97]/1000, data['herf_100'][97]/1000, data['herf_160'][97]/1000, \n",
    "        data['herf_250'][97]/1000, data['herf_350'][97]/1000, data['herf_500'][97]/1000, \n",
    "        flux_wise_w1(data['wisevega_1'][97]), flux_wise_w2(data['wisevega_2'][97]), \n",
    "        flux_wise_w3(data['wisevega_3'][97]), flux_wise_w4(data['wisevega_4'][97]), \n",
    "        flux_2mass_J(data['2mvega_j'][97]), flux_2mass_H(data['2mvega_h'][97]), \n",
    "        flux_2mass_K(data['2mvega_k'][97])]\n",
    "nuF = np.multiply(np.array(nu), np.array(flux))\n",
    "norm_nuF = [i/nuF[0] for i in nuF]\n",
    "\n",
    "# 导入模板（S0）\n",
    "temp_S0 = pd.read_csv(\"S0_template_norm.sed\", sep='\\s+', header=None, names=['wavelength0','Flambda0'])\n",
    "wavelength0 = temp_S0['wavelength0']\n",
    "Flambda0 = temp_S0['Flambda0']\n",
    "lambdaF0 = np.multiply(np.array(wavelength0), np.array(Flambda0))  # lambda F_lambda = nu F_nu\n",
    "a = 0\n",
    "for i in range(len(temp_S0)):\n",
    "    if wavelength0[i] == 700000:\n",
    "        a = i\n",
    "norm_lambdaF0 = [i/lambdaF0[a] for i in lambdaF0]\n",
    "\n",
    "# 绘图\n",
    "plt.figure(figsize=(8,8))\n",
    "plt.loglog()\n",
    "plt.scatter(wavelength, norm_nuF, color=\"r\", linewidth=1)\n",
    "plt.plot(wavelength0, norm_lambdaF0, color=\"b\", linewidth=1)\n",
    "plt.xlabel(r\"$Wavelength(Angstrom)$\",fontsize=20)\n",
    "plt.ylabel(r\"$ Normalized \\ \\nu F_{\\nu} $\",fontsize=20)\n",
    "plt.tick_params(labelsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 再来一个\n",
    "# 导入数据\n",
    "data = pd.read_csv(\"flux_original.csv\")\n",
    "\n",
    "# 计算流量\n",
    "c = 3 * 10**14  # um/s\n",
    "wavelength_um = [70, 100, 160, 250, 350, 500, 3.4, 4.2, 12, 22, 1.23, 1.66, 2.15]\n",
    "nu = [c/i for i in wavelength_um]\n",
    "wavelength = [i*10000 for i in wavelength_um]\n",
    "flux = [data['herf_70'][370]/1000, data['herf_100'][370]/1000, data['herf_160'][370]/1000, \n",
    "        data['herf_250'][370]/1000, data['herf_350'][370]/1000, data['herf_500'][370]/1000, \n",
    "        flux_wise_w1(data['wisevega_1'][370]), flux_wise_w2(data['wisevega_2'][370]), \n",
    "        flux_wise_w3(data['wisevega_3'][370]), flux_wise_w4(data['wisevega_4'][370]), \n",
    "        flux_2mass_J(data['2mvega_j'][370]), flux_2mass_H(data['2mvega_h'][370]), \n",
    "        flux_2mass_K(data['2mvega_k'][370])]\n",
    "nuF = np.multiply(np.array(nu), np.array(flux))\n",
    "norm_nuF = [i/nuF[0] for i in nuF]\n",
    "\n",
    "# 导入模板（S0）\n",
    "temp_S0 = pd.read_csv(\"S0_template_norm.sed\", sep='\\s+', header=None, names=['wavelength0','Flambda0'])\n",
    "wavelength0 = temp_S0['wavelength0']\n",
    "Flambda0 = temp_S0['Flambda0']\n",
    "lambdaF0 = np.multiply(np.array(wavelength0), np.array(Flambda0))  # lambda F_lambda = nu F_nu\n",
    "a = 0\n",
    "for i in range(len(temp_S0)):\n",
    "    if wavelength0[i] == 700000:\n",
    "        a = i\n",
    "norm_lambdaF0 = [i/lambdaF0[a] for i in lambdaF0]\n",
    "\n",
    "# 绘图\n",
    "plt.figure(figsize=(8,8))\n",
    "plt.loglog()\n",
    "plt.scatter(wavelength, norm_nuF, color=\"r\", linewidth=1)\n",
    "plt.plot(wavelength0, norm_lambdaF0, color=\"b\", linewidth=1)\n",
    "plt.xlabel(r\"$Wavelength(Angstrom)$\",fontsize=20)\n",
    "plt.ylabel(r\"$ Normalized \\ \\nu F_{\\nu} $\",fontsize=20)\n",
    "plt.tick_params(labelsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
